Machine Learning-Based Device Modeling and Performance Optimization for FinFETs

The brief introduces a machine learning based framework to model FinFET's I-V and C-V curves with artificial neural networks and to further optimize FinFET's performance on DC and AC characteristics. Our ML-based device model for FinFETs takes V-gs and other nine parameters that define geometry, doping, stress, and work function profile as input variables. Experimental results show that our ML-based device model can predict current and capacitance accurately. Besides, the proposed ML-based performance optimization flow is a promising alternative to the traditional design of experiment method based on technology computer-aided design simulations. Our optimization flow can locate optimized device features accurately and require fewer simulations. Our work demonstrates that ML can perform compact modeling of advanced devices with high accuracy, and the trained ANN models can accelerate performance optimization.

Automated summary

This brief introduces a machine learning-based framework for modeling FinFET's I-V and C-V curves using artificial neural networks, and further optimizing its performance on DC and AC characteristics. Experimental results show accurate prediction of current and capacitance using the ML-based device model. In addition, the proposed ML-based performance optimization flow accurately identifies optimized device features and requires fewer simulations. This work demonstrates the high accuracy of ML in compact modeling of advanced devices and the acceleration of performance optimization using trained ANN models.